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New York, New York, United States
Onsite
In this “hands-on” position, you will design, build, and deploy production-grade AI systems that will power enterprise-scale capabilities across the Clinical Development & Operations organization. This is a high-impact role for a builder who thrives on solving real-world business problems in a complex, data-rich, and regulated environment. You will be instrumental in advancing the practical application of LLMs and agentic AI by identifying high-value use cases, developing reusable workflows, and partnering with stakeholders to drive adoption and impact. Combining deep expertise in software engineering and machine learning, you will take solutions from prototype to production, embedding MLOps best practices to ensure they are reliable, scalable, and reproducible. You will drive business transformation through proactive thought-leadership, innovative analytical capabilities, and the ability to communicate highly complex information in new and creative ways.
Develop and Implement AI Solutions
Build and deploy AI/ML models and solutions that support process-heavy workflows (e.g. protocol feasibility and site selection, study start-up etc.) including documentation, and operational reporting.
Contribute to the automation of manual and repetitive activities to improve speed, quality, and consistency.
Strengthen Operational Decision-Making
Develop predictive, optimization, and scenario-based models to support clinical trial supply forecasting and operational planning.
Create and maintain dashboards and decision-support tools that translate complex data into actionable insights for CD&O leadership and operational teams.
Engineer Production-Grade AI Systems
Implement AI solutions that are aligned with data integrity standards and governance best practices, including model validation, versioning, and monitoring.
Design and implement AI agentic solutions that can plan and execute multi-step workflows.
Build robust, production-ready ML and analytics pipelines with a focus on reproducibility and scalability.
Deploy AI solutions in cloud environments, ensuring reliability, security, and seamless integration with existing systems.
Collaborate Across Disciplines
Partner closely with CD&O line teams, scientists, and Digital partners to ensure that AI efforts remain tightly aligned to real scientific needs and can be deployed in ways that are trusted, scalable, and adopted in day-to-day work.
Champion best practices in AI engineering system lifecycle.
BASIC MINIMUM QUALIFICATIONS
PhD in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 5 years of applied analytical experience with demonstrated impact in operations, automation, business analytics, or decision support OR
Master’s in Computer Science, Machine Learning, Data Science, Software Engineering, AI, or a related discipline and a minimum of 7 years of applied analytical experience with demonstrated impact in operations, automation, business analytics, or decision support.
Strong hands-on experience applying LLMs, generative AI, machine learning, or related AI approaches to real-world workflows, products, or analytical use cases, ideally within R&D, clinical operations or large-scale regulated organizations.
Experience building practical, reusable workflows or systems rather than one-off analyses, with strong implementation skills in Python and modern AI / ML tooling.
Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled workflows.
Experience working directly with domain users or stakeholders to translate ambiguous needs into useful technical solutions, with evidence of strong collaboration and communication skills.
TECHNICAL SKILLSET
AI Engineering/ Framework: Strong hands‑on experience with Python building ML/DL with libraries (e.g. TensorFlow, PyTorch, Keras, Scikit-learn), and LLM‑based systems and agentic frameworks including RAG architectures, prompt engineering, embeddings, fine‑tuning, evaluation and orchestration (e.g. ADK, LangChain, LangGraph, Vertex AI, Claude).
Software & Data Engineering/ Framework: Experience with Java, JavaScript/TypeScript, React, FastAPI, SQL/PostgreSQL, Snowflake, S3, and enterprise data and knowledge systems (e.g. BigQuery, Neo4j).
Cloud, DevOps & MLOps: Proficient with Git, Docker, CI/CD, and cloud platforms (AWS/GCP/Azure), with a strong focus on reproducibility, deployment, monitoring, and production‑ready MLOps.
PREFERRED QUALIFICATIONS
Experience in life sciences, pharma, biotech, systems biology, immunology, translational science, omics, or related research environments.
Experience operating across scientific and technical disciplines, with enough domain fluency to engage credibly with scientists while still bringing a strong applied-AI builder mindset.
Work Location Assignment: This is a hybrid role requiring you to live within commuting distance and work on-site an average of 2.5 days per week.
Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.
NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
Occasional travel may be required to collaborate with colleagues across Pfizer sites, participate in workshops, support adoption activities, or engage with internal and external partners.
Relocation assistance may be available based on business needs and/or eligibility.

Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.

Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.
EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States.
Pfizer endeavors to make www.pfizer.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please email disabilityrecruitment@pfizer.com. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

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